# AI-informed Signaling Factor Design for in vitro Rejuvenating Mesenchymal Stromal Cells

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $371,598

## Abstract

ABSTRACT
 While mesenchymal stromal cells (MSCs) hold enormous promise for treating many challenging
diseases, a major barrier toward clinically meaningful MSC therapies is the inability to produce potent MSCs
consistently. Specifically, in vitro cultured MSCs often rapidly enter senescence in which they lose their potency.
In contrast to natural in vivo senescence, such in vitro aging has been shown to be largely driven by misregulated
metabolic signaling in culture. To address this grand challenge, many signaling pathways (e.g., FGF, ATM, SRT,
mTOR, EGF, DDR2) have been identified for regulating senescence-related processes. Building upon these
discoveries, this R35 MIRA proposal aims to develop an innovative engineering approach to delaying the MSC
senescence process by collectively adjusting these signaling pathways. Specifically, we hypothesize that a
sufficiently trained AI model can predict the signaling factor combination that effectively slows down or even
reverts the senescence-related transcriptional drift. To achieve such a goal, my research aims to address three
knowledge/technology gaps in MSC engineering (Fig. 1B): 1) how to accurately phenotype live MSCs (e.g.,
characteristics, proliferation, and potency); 2) how to predict signaling factors that dictate the desired
transcriptional response; and 3) how to ensure the robustness of such predictions.
 In challenge 1, this proposal will expand our previously developed AI platform by developing approaches
to acquiring large-scale AI training data that cover a wide range of MSC phenotypes and interpreting black-box
deep learning models. The goal is to decipher the morphology-gene expression relationship in MSCs. In
challenge 2, we will utilize deep learning to identify the signaling factor combination and predictively adjust gene
expression in MSCs. In the third challenge, we will develop algorithms that improve the robustness of AI models
and turn our proof-of-concept AI platforms into reliable tools for practical clinical utilizations. The immediate
outcome of our proposed research will lead to a high-throughput phenotyping and engineering platform of MSCs.
The proposed experimental platform will also enable us to establish better understandings in MSC
mechanobiology and senescence signaling interactions.

## Key facts

- **NIH application ID:** 10707372
- **Project number:** 5R35GM146735-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Neil Lin
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $371,598
- **Award type:** 5
- **Project period:** 2022-09-21 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10707372

## Citation

> US National Institutes of Health, RePORTER application 10707372, AI-informed Signaling Factor Design for in vitro Rejuvenating Mesenchymal Stromal Cells (5R35GM146735-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10707372. Licensed CC0.

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